%0 Generic %1 citeulike:1452800 %A Wan, E. %A van der Merwe, R. %D 2000 %K kalman-filter statistics time-series %T The Unscented Kalman Filter for Nonlinear Estimation %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.7495 %X The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system, estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network), and dual estimation (e.g., the Expectation Maximization (EM) algorithm) where both states and parameters are estimated simultaneously. This paper points out the flaws in using the...

@misc{citeulike:1452800, abstract = {{The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system, estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network), and dual estimation (e.g., the Expectation Maximization (EM) algorithm) where both states and parameters are estimated simultaneously. This paper points out the flaws in using the...}}, added-at = {2019-06-18T20:47:03.000+0200}, author = {Wan, E. and van der Merwe, R.}, biburl = {https://www.bibsonomy.org/bibtex/2d0f56dc7aaa6c94606a4f41a649d1f78/alexv}, citeulike-article-id = {1452800}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.7495}, interhash = {31ec41520b5443e7d2d147b9afb93501}, intrahash = {d0f56dc7aaa6c94606a4f41a649d1f78}, keywords = {kalman-filter statistics time-series}, posted-at = {2007-07-12 18:34:15}, priority = {0}, timestamp = {2019-08-24T00:19:49.000+0200}, title = {{The Unscented Kalman Filter for Nonlinear Estimation}}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.7495}, year = 2000 }